Pooling of Samples to Increase Testing Capacity for COVID-19

Test, trace and isolate are the main pillars of the containment strategies promoted by epidemiologists in the COVID-19 pandemic. Equipment, material and labour required for testing is, however, limited, making it a challenge to adopt testing at a large scale. Pooling of samples has the potential to reduce the number of tests required for screening a population with a low infection prevalence. We provide a detailed analysis of a well-known pooling strategy called two-stage pooling which involves testing pools of a fixed size. We show that, while this approach can potentially reduce the number of tests, evaluating its cost effectiveness and configuring it optimally require existence of a reliable estimate of prevalence in the population. In the absence of such information, we propose inferring a prior distribution of the underlying prevalence using a combination of expert opinion and a limited exploratory testing of the population, and applying it with either a two-stage fixed pooling strategy, or a multi-stage adaptive pooling strategy. We explain how each of these strategies can be applied, propose algorithms for finding their corresponding optimal pool size, and identify the situations under which each of these strategies is preferred

Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

Specialty clinics provide specialized care for patients referred by primary care physicians, emergency departments, or other specialists. Urgent patients must often be seen on the referral day, whereas nonurgent referrals are typically booked an appointment for the future. To deliver a balanced performance, the clinics must know how much “appointment capacity” is needed for achieving a reasonably quick access for nonurgent patients. To help identify the capacity that leads to the desired performance, we model the dynamics of appointment backlog as novel discrete-time bulk service queues and develop numerical methods for efficient computation of corresponding performance metrics. Realistic features such as arbitrary referral and clinic appointment cancellation distributions, delay-dependent no-show behaviour, and rescheduling of no-shows are explicitly captured in our models. The accuracy of the models in predicting performance as well as their usefulness in appointment capacity planning is demonstrated using real data. We also show the application of our models in capacity planning in clinics where patient panel size, rather than appointment capacity, is the major decision variable

An Integrated Approach to Demand and Capacity Planning in Outpatient Clinics

An outpatient clinic serving two independent demand streams, one representing advance booking requests and the other same-day requests, is considered. Advance requests book their appointments through an electronic booking system for a future day, and same-day requests are served on the day they arise. A compact policy formulation is proposed that incorporates major operational levers suggested in the literature. It combines a slot publication policy, which specifies the pattern under which slots are released to the booking system, with an expediting policy that adjusts the daily workload of advance patients. Relying on a wide range of numerical experiments, a heuristic search method is developed for finding the joint publication and expediting policies, minimizing the cost of overtime slots whilst ensuring a waiting and an access constraint is met. Several managerial insights are derived using a combination of illustrative and real data, highlighting the importance of taking an integrated approach towards the operational levers captured by our policy formulation

The impact of geological heterogeneity on horizontal well-triplet performance in CO2-circulated geothermal reservoirs

CO2 circulated geothermal production can be integrated with CO2 geological sequestration as a utilization method to offset cost. Investigation of heterogeneity impact is limited to CO2 sequestration and its effect on CO2 circulation and associated heat recovery is unclear. This study is aimed to improve the understanding of this problem by numerical experiments. A set of spatially correlated heterogeneous porosity fields is generated using a variety of geostatistical parameters, i.e., variance, correlation lengths, anisotropy and azimuth. Heterogeneous fields of intrinsic permeability and initial/residual water saturation are derived from porosity using equations regressed from a field dataset. Twenty combinations of injection pressure and well space obtained by Latin-Hypercube sampling are deployed in each heterogeneous field, generating a suite of numerical geothermal reservoir models. Performance indicators, including lifespan, net stored CO2 , produced heat flux, and total recovered heat energy in lifespan, are calculated from each model simulation. The simulation results suggest that geologic heterogeneity could develop high-permeable CO2 flow paths, causing bypass of the hot low-permeable zones, shortened lifespan and reduced total recovered heat energy. Depending on the azimuth, anisotropy can create either flow barriers or preferential flow paths, increasing or decreasing heat sweeping efficiency. The relative angle between horizontal wells and the axis of maximum continuity of the heterogeneity can be optimized to maximize heat recovery efficiency. These finds provide useful insights of interplay between geological heterogeneity, well placement and operation of CO2 circulated geothermal production.Cited as: Chen, M., Al-Saidi, A., Al-Maktoumi, A., Izady, A. The impact of geological heterogeneity on horizontal well-triplet performance in CO2-circulated geothermal reservoirs. Advances in Geo-Energy Research, 2022, 6(3): 192-205. https://doi.org/10.46690/ager.2022.03.0

Uniform fractional part: a simple fast method for generating continuous random variates

A known theorem in probability is adopted and through a probabilistic approach, it is generalized to develop a method for generating random deviates from the distribution of any continuous random variable. This method, which may be considered as an approximate version of the Inverse Transform algorithm, takes two random numbers to generate a random deviate, while maintaining all the other advantages of the Inverse Transform method, such as the possibility of generating ordered as well as correlated deviates and being applicable to all density functions, regardless of their parameter value

Deriving optimal operational policies for off-stream man-made reservoir considering conjunctive use of surface- and groundwater at the Bar dam reservoir (Iran)

Study region: The off-stream artificial Bar lake, built in 2015 to store the flood flows of the Bar river for domestic and industrial needs and with the objective to intentionally recharge the aquifer, is situated in the Razavi Khorasan province (Iran). Study focus: We present a methodology, based on the combination of a MODFLOW groundwater flow model for estimating seepage rates, and an optimization model, for the management and operation of an artificial reservoir considering surface/groundwater interactions for satisfying 12 Mm3/year of water demand. We simulated the reliable amount of water that can be supplied from the reservoir, considering reservoir seepage, maximizing water supply yields subject to the water supply reliability requirements, and the additional intentional volume of groundwater recharge. New hydrological insights for the region: Our results demonstrate the reliability of conjunctive use of surface-and ground-water in water scarce areas by exploiting reservoir infrastructures with relevant leakage losses, also for creating additional aquifer storage. In such systems, man-induced changes of lake stages can significantly affect the volume of water that seeps through the lakebed. The aquifer, under managed aquifer recharge operations, may then provide the resource not satisfied by the reservoir release, fulfilling 100 % reliability of water supply. The conjunctive use of surface- and ground-water, by improving water security, may open new sustainability views for leaking reservoirs, even if they were not initially designed for increasing aquifer recharge, in many areas worldwide

Determination of Sustainable Tourism Development Strategies in Coastal Areas with Emphasis on Nature-based Tourism, Coastal Area of Bandar Mogham to Bandar Hasineh in Hormozgan Province

Nature-based tourism as one of the types of tourism can play an important role in the sustainable development of regions and also have important effects on improving the physical and mental health of tourists. Bandar Lengeh County has several natural capacities such as unique sandy, rocky and coral beaches, numerous islands, salt domes and unique mountain landscapes that indicate the proper capacity of this county for the development of nature-based tourism. However, it's potential and actual capacities have not yet been used effectively. The aim of this study is to determine the strategies for the development of sustainable tourism with an emphasis on nature-based tourism in the western region of Bandar Lengeh County. For this purpose, first, the internal factors (strength and weakness) and external (opportunity and threat) were determined using SWOT technique and the opinion of experts and then based on them, the strategies for developing nature-based tourism in the region have been identified, Finally, the strategies were ranked using the quantitative strategic planning matrix (QSPM) technique. The results show that strategies such as providing nature-based tourism equipment and facilities on the region's coasts, providing equipment for water sports and recreation on the region's coasts, and guiding tourists from Fars province, Kish island and the Persian Gulf countries to the region are more important in order than other strategies. The findings of this study can be considered by managers, decision-makers and planners in order to plan and develop nature-based tourism and subsequently achieve sustainable development in this region

Conflict resolution in the multi-stakeholder stepped spillway design under uncertainty by machine learning techniques

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A Clustered Overflow Configuration of Inpatient Beds in Hospitals

Problem Definition: The shortage of inpatient beds is a major cause of delays and cancellations in many hospitals. It may also lead to patients being admitted to inappropriate wards, whereby resulting in a lower quality of care and a longer length of stay. Academic/Practical Relevance: Investment in additional beds is not always feasible. Instead, new and creative solutions for a more efficient use of existing resources must be sought. Methodology: We propose a new configuration of inpatient beds which we call the clustered overflow configuration. In this configuration, patients who are denied admission to their primary wards as a result of beds being fully occupied are admitted to overflow wards, with each designated to serve overflows from a certain subset of specialties and providing the same quality of care as in primary wards. We propose two different formulations for partitioning and bed allocation in the proposed configuration: one minimizing the sum of average daily costs of turning patients away and nursing teams, and another minimizing the numbers turned away subject to nursing cost falling below a given threshold. We heuristically solve instances from both formulations. Results: Applying the models to real data shows that the configurations obtained from our models compare very well with the other configurations proposed in the literature, provided that patients' willingness to wait is relatively short. Managerial Implications: The proposed configuration provides the combined advantages of the dedicated configuration, wherein patients are only admitted to their primary wards, and the exible configuration, in which all specialties share a single ward. On the other hand, it restricts the adverse impacts of pooling and minimizes cross-training costs through appropriate partitioning and bed allocation. As such, it serves as a viable alternative to existing inpatient configurations

Reconfiguration of Inpatient Services to Reduce Bed Pressure in Hospitals

Healthcare systems around the world are facing an inpatient bed crisis. This was highlighted more than ever during the recent COVID-19 pandemic. The consequences of bed shortage are substantial for both patients and staff. Finding innovative ways to improve the utilization of the existing bed base is therefore of significant importance. We focus on reconfiguration of inpatient services as a cost-effective solution to bed pressure in hospitals, and propose a comprehensive methodology for finding a low-cost configuration given a total number of beds, a set of specialties, and a finite or infinite waiting time threshold for patients. This involves developing novel approximations for performance evaluation of overflow delay and abandonment systems, and embedding them within heuristic search algorithms. We apply our reconfiguration methodology on inpatient data from a large UK hospital. Simulation experiments show that the configurations proposed by our methodology can result in significant savings compared to the existing configuration, and that a clustered overflow configuration is likely to produce the best results in many scenarios